Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018 (ICPSR 39471)

Version Date: Aug 26, 2025

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Principal Investigator(s):
Alan Hubbard, University of California-Berkeley

https://doi.org/10.3886/ICPSR39471.v1

Version V1

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Summary

Electronic health records store a lot of data about a patient. These data often include age, health problems, current medicines, and lab results. Looking at these data may help doctors treating patients after a trauma predict how likely it is that they will respond well to a treatment and survive. This information can help doctors make better treatment decisions. But first, researchers need to figure out how to combine and analyze data to make accurate predictions. In this study, the research team created new statistical methods to combine data from patient records. They used these methods to predict patient health outcomes. Then the team used health record data collected from patients in hospital trauma centers to test their predictions.

To access the methods and software, please visit the following GitHubs:

Citation

Hubbard, Alan. Semiparametric Causal Inference Methods for Adaptive Statistical Learning in Trauma Patient-Centered Outcomes Research [Methods Study], 2013-2018. Inter-university Consortium for Political and Social Research [distributor], 2025-08-26. https://doi.org/10.3886/ICPSR39471.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1306-02735)

Subject Terms

Geographic Coverage

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2013 -- 2018
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Study Purpose

To develop methods, using machine-learning platforms, for incorporating clinical data on many clinical variables to accurately and efficiently predict the effects of interventions on trauma patients. The study aims were the following:

  1. Develop diagnostic (prediction) scores via SuperLearner for each of the trauma studies. Estimate the relative prediction accuracy of competing algorithms via cross-validation tools, such as cross-validated area under the curve.
  2. Develop and evaluate variable importance estimators, which can potentially quantify the differentiable impact of treatments within covariate groups.
  3. Develop and evaluate algorithms and software for deriving optimal patient rules using machine learning methods.

Study Design

This study developed and evaluated methods to improve automated assessments of prognosis and predictions of treatment effects for acute trauma care patients to support individualized recommendations for patient care.

Researchers used a machine-learning framework to apply multiple statistical methods for predicting the effects of interventions. The researchers used:

  • SuperLearner, an analytic approach that generates optimal predictions of clinical outcomes and treatment effects using a library of prediction algorithms
  • Causal inference models to identify optimal predictor variables
  • Targeted maximum likelihood estimation quantifying variable importance related to clinical outcomes and treatment effects

Researchers developed and tested machine-learning methods using both simulated data and data from four patient-outcome studies in trauma units from major hospitals across the United States.

The data sets included information on vital signs, lab results, and trauma severity. To test applications of the methods, researchers constructed both baseline and dynamic prediction algorithms for patient outcomes, including hemostasis, or blood clotting, and death.

Data Source

Simulated data, 4 data sets from acute trauma studies for empirical analysis: Activation of Coagulation and Inflammation in Trauma (ACIT) study; Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study; Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study; Pragmatic Randomized Optimal Platelet and Plasma Ratios (PROPPR) trial

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Original Release Date

2025-08-26

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Notes

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.